Welcome to the Advanced Generative Adversarial Networks (GANs) course! In this section, we will delve into the intricate details of GANs, their architecture, and their applications.
What are GANs?
Generative Adversarial Networks, or GANs, are a class of neural networks that consist of two main components: a generator and a discriminator. The generator creates data that tries to fool the discriminator, while the discriminator tries to distinguish between real data and generated data. This adversarial process allows GANs to generate high-quality, realistic images.
Course Outline
Introduction to GANs
- History and motivation
- Basic architecture
- Key components
GANs in Practice
- Training and evaluation
- Common pitfalls and solutions
- Real-world applications
Advanced GANs Techniques
- Improved architectures
- Style transfer
- Text-to-image generation
Resources
For further reading, we recommend checking out our Deep Learning Basics course. It provides a solid foundation for understanding GANs and their underlying principles.
Images
GAN Architecture
Style Transfer
Text-to-Image Generation